ICE cores are variations of multivariate optical elements (“MOE”) which were originally targeted for qualitative and quantitative analysis of physical or chemical properties of interest in chemometrics. In recent years, ICE technologies have been developed for various applications, including the Oil and Gas Industry in the form of optical sensors on downhole or surface equipment to evaluate a variety of fluid properties. ICE cores typically consist of multiple physical layers with different refractive indexes in the film material, wherein their optical or spectroscopic characteristics, if designed properly, can be transformed into effective inputs for linear and nonlinear multivariate calibration.
An optical computing device is a device configured to receive an input of electromagnetic radiation from a substance or sample of the substance and produce an output of electromagnetic radiation from a processing element. The processing element may be, for example, an ICE core. Fundamentally, optical computing devices utilize optical cores to perform regression calculations, as opposed to the hardwired circuits of conventional electronic processors. When electromagnetic radiation interacts with a substance, unique physical and chemical information about the substance is encoded in the electromagnetic radiation that is reflected from, transmitted through, or radiated from the sample. This information is often referred to as the substance's spectral “fingerprint.” Thus, the optical computing device, through use of the ICE core, is capable of extracting the information of the spectral fingerprint of multiple characteristics or analytes within a substance and converting that information into a detectable output regarding the overall properties of a sample.
Optical computing devices are often characterized in terms of each of their optical components. The total system throughput can be estimated as the product of these components, with each component imposing its individual effect on the spectral throughput of the device. However, the spectral throughput of compiled optical systems often differ from the modeled spectral throughput due to a number of factors, such as lens aberrations, variation in optical elements, variation in optical element position, and other random errors, all of which are not accounted for in the model. In addition, systematic errors (in which there is a constant error in the spectral profile) are also unaccounted for in the model. As a result, the assembled optical computing device will contain throughput errors which may result in performance degradation in prediction of sample characteristics.
Accordingly, there is a need in the art for methods by which to correct for throughput errors in optical systems.